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Using Stable Diffusion with Python

Using Stable Diffusion with Python

By : Andrew Zhu (Shudong Zhu)
4.8 (5)
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Using Stable Diffusion with Python

Using Stable Diffusion with Python

4.8 (5)
By: Andrew Zhu (Shudong Zhu)

Overview of this book

Stable Diffusion is a game-changing AI tool that enables you to create stunning images with code. The author, a seasoned Microsoft applied data scientist and contributor to the Hugging Face Diffusers library, leverages his 15+ years of experience to help you master Stable Diffusion by understanding the underlying concepts and techniques. You’ll be introduced to Stable Diffusion, grasp the theory behind diffusion models, set up your environment, and generate your first image using diffusers. You'll optimize performance, leverage custom models, and integrate community-shared resources like LoRAs, textual inversion, and ControlNet to enhance your creations. Covering techniques such as face restoration, image upscaling, and image restoration, you’ll focus on unlocking prompt limitations, scheduled prompt parsing, and weighted prompts to create a fully customized and industry-level Stable Diffusion app. This book also looks into real-world applications in medical imaging, remote sensing, and photo enhancement. Finally, you'll gain insights into extracting generation data, ensuring data persistence, and leveraging AI models like BLIP for image description extraction. By the end of this book, you'll be able to use Python to generate and edit images and leverage solutions to build Stable Diffusion apps for your business and users.
Table of Contents (29 chapters)
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Free Chapter
1
Part 1 – A Whirlwind of Stable Diffusion
8
Part 2 – Improving Diffusers with Custom Features
15
Part 3 – Advanced Topics
21
Part 4 – Building Stable Diffusion into an Application

Exploring Stable Diffusion XL

After the not-very-successful Stable Diffusion 2.0 and Stable Diffusion 2.1, July 2023 saw the launch of Stability AI’s latest release, Stable Diffusion XL (SDXL) [1]. I eagerly applied the model weights data as soon as registration was open. Both my tests and those conducted by the community indicate that SDXL has made significant strides forward. It now allows us to generate higher-quality images at increased resolutions, vastly outperforming the Stable Diffusion V1.5 base model. Another notable enhancement is the ability to use more intuitive “natural language” prompts to generate images, eliminating the need to cobble together a multitude of “words” to form a meaningful prompt. Furthermore, we can now generate desired images with more concise prompts.

SDXL has improved in almost every aspect compared to the previous versions, and it is worth the time and effort to start using it for better and stable image generation...

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